Transport Vehicle Capacity Maximization Logistics System and Method of Same

ABSTRACT

Disclosed is a system for substantially optimizing logistics for loading vehicles and transporting goods and a method of accomplishing the same.

RELATED APPLICATIONS

This application is a continuation of application Ser. No. 12/906,634,filed Oct. 18, 2010, which is a continuation of application Ser. No.11/110,781, filed Apr. 18, 2005, which is a Continuation-in-Partapplication of application Ser. No. 09/751,144 filed on Dec. 29, 2000,which issued as U.S. Pat. No. 6,937,992, which are hereby incorporatedby reference in their entirety.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[Not Applicable]

[MICROFICHE/COPYRIGHT REFERENCE]

[Not Applicable]

TECHNICAL FIELD OF THE INVENTION

The presently described technology relates to an inventory managementsystem wherein inventory levels are controlled.

BACKGROUND OF THE INVENTION

Within the United States, trucks carry the majority of freight, and alarge portion of the trucking industry concentrates on less thantruckload (LTL) shipments. This is understandable, since many shipmentsare not large enough to fill a truck, but are too large to economicallyjustify shipment by a parcel carrier such as UPS. As a result, manytrucks (and, by extension, other freight transport modalities such ascontainers, trains, barges, ships and airplanes) carry less than a fullload. Since the cost of operating a truck is largely unaffected by thesize of its load, this unused capacity represents a substantial economicinefficiency. Without loss of generality, the following argument can beextended to other forms of freight transportation, including trains,barges, airplanes, containers, and ships.

Another industry that has experienced similar problems is the airlineindustry, particularly since it was deregulated a quarter of a centuryago. Upon deregulation, existing airlines faced competition from manynew low-cost entrants. During the decade that followed, severalincumbent airlines failed (Eastern, PanAm, National). Others, such asAmerican, United, and Delta, gained market share. These incumbentssurvived and prospered largely by adopting three technologies:hub-and-spoke topologies, yield management, and frequent flier programs.

Hub-and-spoke networks evolved from earlier point-to-point service,channeling passengers from their point of origin to one of several hubs,from which other flights depart. This approach, by separating access tothe network of flights from travel within it, allows airlines to offermore departures between an origin and destination, while increasingcapacity utilization. The downside is that service now includes asmaller proportion of direct flights, which imposes a convenience coston the consumer, or an inconvenience due to time constraints.

Another innovation developed by airlines was that of yield management,or the use of fare structure to maximize capacity utilization andrevenue per flight. Yield management exploits the fact that airlinepassengers generally fall into two categories, tourists and businesstravelers. Tourists are typically price sensitive, but are willing toplan their trips in advance and spend weekends away from home. Businesstravelers, in contrast, tend to be more willing to pay more to fly, butneed to schedule their flights on shorter notice and are less willing tospend their weekends on the road. These different attributes haveencouraged airlines to adopt fare policies that differentiate betweenthese two groups. As a result, two otherwise identical tickets on thesame flight may have vastly different fares, depending on the time andconditions of purchase. Airlines use fares to maximize yield becausethey cannot directly control the demand for seats, and can only usefares as an indirect mechanism to do so.

Finally, airlines developed frequent flyer programs to exploit anattribute of how business-related airline tickets are purchased and toencourage customer loyalty. One well-understood effect of frequent flierprograms is that they encourage brand loyalty, and therefore reduceprice competition between airlines. What drives these programs, however,is the separation of the functions of selecting and paying for a flightfor business. In particular, the typical business traveler chooses thecarrier, but the employer pays for the ticket. Frequent flier programsthus work by rewarding the traveler directly for how he or she spendsthe employer's money. Airlines are not alone in using this marketstructure—other examples include health insurers (who pay for medicalservices specified and performed by providers) and McDonald's (whichmarkets toward children, even though parents pay).

While the freight transportation and airline industries share manyattributes (network structure, different priorities for services, andseparation of purchase of and payment for services), the freightindustry has been slow to adopt many airline innovations. In trucking,which accounts for the majority of freight hauled in the United States,the industry is segmented into truckload and less than truckload (LTL)hauling. Truckload firms operate by running trucks that are usuallydedicated to a single customer on a point-to-point basis. Less thantruckload firms (which represent a majority of the trucking industry)move loads between points, but put multiple loads on the same truck. ByU.S. Department of Transportation definition, the term “truckload”includes motor carriers operating with loads that weigh either more than10,000 lbs., or loads that require exclusive use of the truck. Thisexcludes parcel carriers such as UPS or Federal Express, and the PostalService. Other groups classify LTL to include service for loads thatweigh between 250-12,000 lbs. In this respect, an LTL firm operates muchlike an airline, hauling disparate loads while trying to maximizerevenue per truck.

Despite the similarities, the LTL trucking industry has failed to keeppace with the airline industry in terms of capacity utilizationpolicies. One area in which the LTL carriers have emulated airlines isthe adoption of hub-and-spoke topologies, known in the industry asfreight consolidation. Freight consolidation operates by having the LTLcarrier pick up the load from the shipper and take it to a nearbywarehouse owned by the carrier. The load then waits until enough cargohas accumulated from other orders to justify sending a full truck toanother warehouse near the destination. From that point, a third trucktakes the load from the warehouse to the final destination.Consolidation thus allows an LTL carrier to reduce the number of milestraveled by partially full trucks. The disadvantage to this approach,however, is that a load to be consolidated must wait until enough goodsshare a common destination to justify sending a full truck.

Another area in which the freight industry lags behind its air transportcounterparts is in yield management. The reason why LTL consolidatorsmust rely on flexible shipping schedules is because they have no directinfluence on demand. This is because carriers generally have no directcontrol over the orders they ship—customers do. As a result, there is nodirect link between the carrier cost and level of service, on the onehand, and the order flow, on the other.

Several attempts have been made to more closely match order flow withtransportation capacity utilization. One approach, demandsynchronization, calls for customers to place their orders with amanufacturer at only a specified time, in order to allow themanufacturer to send the orders out in a more economically efficientbatch. A more extreme approach, vendor-managed inventory (VMI), takesthe customer out of the ordering process in toto. Under VMI, themanufacturer reviews the customer's inventory, arranges for orders, andsends the goods to the customer with only the customer's tacit approval.Under VMI, the responsibilities of ordering and fulfillment are bothassumed by the manufacturer, who minimizes logistics costs, subject toagreed-upon standards for inventory levels and quality of service.

VMI offers many advantages to its users. Both parties gain by reduceddata entry errors, faster processing, and better service for customers.Combining responsibilities for ordering and order fulfillment helps makelogistics costs an explicit part of the cost calculation. Transportationassets have greater capacity utilization, and loading dock congestion,and warehouse congestion are alleviated.

VMI also offers benefits that accrue solely to the customer. Thecustomer experiences a reduction in order processing time, whichproduces several beneficial effects. Fill rates from the manufacturerare improved. The level of service increases, since the system can bemore responsive to changes in demand. Inventory levels decrease.Additionally, planning and ordering costs are passed back to thesupplier, and are therefore eliminated. Finally, since the manufacturerbears responsibility for fulfillment, it is more focused than ever oncustomer satisfaction.

The supplier also gains from VMI. It gains visibility with customers.Access to customer inventory data makes it easier to forecast demand.Customer ordering errors (and consequent returns) are reduced. Finally,since the manufacturer has control of both orders and freight costs, anincentive exists to coordinate them to reduce total costs.

VMI carries with it several disadvantages, however. First, the customercedes control of a critical process—purchasing—to a supplier. Such aclose relationship requires a degree of trust that is rare in thebusiness world. Second, VMI is based around the one to one relationshipbetween supplier and customer. As a result, while VMI relationships canbe economically beneficial, they typically require a large amount ofscale to justify, given the fixed costs associated with implementing theVMI solution, and the level of traffic required to fill trucks on aregular basis.

Thus, while VMI may seem to cure all logistical ills, it is difficult toimplement universally. As a result, many customer relationships willcontinue to be based on the traditional model of the customer orderinggoods and the supplier paying for freight. In this case, the freightindustry has nothing comparable to the third airline achievement—theincentives provided by frequent flier programs. Frequent flier programswork because they target a party, the flier (typically a businessflier), who has purchasing authority, but does not have to pay for whatis bought. Similarly, in many industries, the customer, when buying aproduct with a delivered price, indirectly purchases freight servicesfrom a carrier, but the manufacturer pays for it. While airlines havebeen able to address this market characteristic, the closest that thefreight industry has come in approaching this mechanism is to use VMI toconsolidate these functions.

So, although the LTL industry must deal with a set of problems similarto that faced by the airline industry—network topology, unpredictabledemand, and a lack of incentives to coordinate behavior between seller,customer and carrier, it has failed to evolve an effective response.This failure is all the more telling since the freight industry, unlikethat for passengers, can directly control cargo; whereas airlines cannotforce people, no matter how delightful the destination, onto airplanes.

Previous efforts to optimize logistics have been deductive in nature,accepting the state of the world as a given and attempting to drivetowards a more efficient outcome. The presently described technologytakes an inductive approach: given that a logistically ideal world is tobe achieved, what is the best way to achieve it? As a result, thepresently described technology combines variants of the airlineinnovations of hub-and-spoke topology, order flow control, and tradeincentives to optimize the use of logistics assets in a new andeconomically useful way.

BRIEF SUMMARY OF THE INVENTION

The presently described technology is useful for substantiallyoptimizing a shipment of merchandise, which may, in one embodiment,utilize a method having the steps of:

determining at least one product required to be maintained in inventoryby at least two receivers in response to data received from thereceivers by at least one shipper; and

substantially optimizing the shipment of the product by determining oneor more substantially maximum loads of one or more transport vehicles atleast in part by calculating an amount of the product for shipment fromthe at least one shipper by one or more transport vehicles from theshipper to the receivers that reduces logistics costs and maintains theinventory within the amount of product required to be maintainedaccording to an algorithm employing one or more metrics and the data.

The presently described technology is also useful for substantiallyoptimizing a shipment of merchandise, and, in another embodiment, theshipment being to a first receiver maintaining a first inventory ofmerchandise at a first receiver location and to a second receivermaintaining a second inventory of merchandise at a second receiverlocation, the shipment of merchandise being from a first shipper locatedat a first source location and from a second shipper located at a secondsource location. In such an environment, the presently describedtechnology may comprise:

determining the limits of the merchandise required to be maintained forthe first inventory according to one or more first metrics in responseto first data received from the first receiver;

determining the limits of the merchandise required to be maintained forthe second inventory according to one or more second metrics in responseto second data received from the second receiver; and

determining one or more maximum loads of one or more transport vehiclesat least in part by calculating an amount of merchandise for shipmentfrom the first and second shippers by the one or more transport vehiclesfrom the first and second sources to the first and second receivers thatreduces logistics costs and that results in shipment of merchandisewithin the limits of merchandise required to be maintained for the firstinventory and within the limits of merchandise required to be maintainedfor the second inventory according to an algorithm employing at leastthe one or more first metrics, the one or more second metrics, the firstdata and the second data.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram of the current order-shipping model.

FIG. 2 is a block diagram of the current manufacturer-distributor model.

FIG. 3 is a block diagram of a multiple manufacturer multipledistributor model.

FIG. 4 is a block diagram of a vendor managed inventory model.

FIG. 5 is one embodiment of the presently described technology.

FIG. 6 is another embodiment of the presently described technology.

FIG. 7 is a block diagram of a remote vendor managed inventory model.

FIG. 8 is another embodiment of the presently described technology.

FIG. 9 is another embodiment of the presently described technology.

FIG. 10 is a message flow diagram of the presently described technology.

FIG. 11 is a server block diagram of the system of the presentlydescribed technology.

FIG. 12 demonstrates another feature of the presently describedtechnology.

DETAILED DESCRIPTION OF THE INVENTION

As described herein, the term shipper is used to denote a company thatships products or goods to another, such as a receiver. However, for thepurpose of clarity only, and without being limited thereto, someembodiments may describe shippers as manufacturers and receivers asdistributors. In some embodiments, receivers may be customers also. Itis understood that the presently described technology is not so limitedbetween manufacturers and distributors. As also explained herein,shippers are different legal entities, or companies that operateseparately.

As used herein, the term “vehicle” is used to denote any modality ofshipping or anything capable of carrying goods. It can include, but isnot limited to, ships, barges, vans, trailers, cars, trucks, trains,airplanes, containers, pallets, cubes, etc.

Also as used herein, the term “product” can mean either the sameproduct(s), a different product(s), or a newly created product(s). Inother words, just because product X is initially ordered, does not meanthat any further optimized product must be product X, as it could beproducts Y or Z, etc. The term “product” is also interchangeable withthe terms, “merchandise,” “good(s)” or “item(s).”

For all embodiments, it should be noted that “capacity”, also usedsynonymously as “load”, can be measured as volume capacity (such ascubic capacity, or height, weight, or length) or by weight capacity(such as poundage), or by pallet footprint, or by number of cubes,cartons, containers, boxes, or the like. Also, it should also be notedthat capacity is also a function of the size of the vehicle. Forexample, in the trucking industry, moving from single unit trucks totruck-trailer combinations or semi-trailer combinations can increasecapacity. Furthermore, multi-trailer combinations such as double ortriple trailer combinations can affect capacity. Trailer size may range,but may include the standard 28 foot, 28.5 foot, or 48 foot trailers.Similarly, cargo hold size, and the number of containers placed abovedeck may affect capacity in boats/ships or other transport vehicles.Similarly, train capacity is a function of the number of cars, boxcars,liquid container cars, etc. In addition, the number of pallets (usually,but not exclusively, 44 pallets per standard truck) is another possibleconstraint.

FIGS. 1 through 4 show embodiments of the prior art. In FIG. 1, amanufacturer 10 such as manufacturer M1 receives orders from a receiver,such as customer 12 and then ships the merchandise to the customer 12.The customer 12 can place many orders with other manufacturers 10 n,such as manufacturer M2 or M3. In this regard, the logistical issuesinvolve multiple shipments from a plurality of manufacturers 10 n to asingle customer 12. If the customer 12 orders too little merchandise,then the manufacturer 10 will ship a partial vehicle load to thecustomer 12. From the customer's vantage, orders must be placed witheach individual manufacturer and the customer 12 receives shipments froma plurality of manufacturers. This becomes an administrative problem forthe customer. It is a shipping problem also for the manufacturer sinceit may have to ship small volumes of merchandise to many customers. Thelarge number of small shipments can clog loading docks. Finally, it iswell established that the cost per pound is inversely related to theload of the vehicle. This implies that the separation of order control(performed by the customer) and payment of freight cost (performed bythe seller) can lead to outcomes that would be more costly than if bothparties were better coordinated.

FIG. 2 demonstrates another embodiment of the prior art and is a furtherrefinement of the embodiment described further in FIG. 1. Shown is adistributor 14 that interfaces between manufacturers and the customer.In this example, the distributor 14 receives orders from the customer 12and ships merchandise to the customer 12 directly. The distributor 14may ship many types of merchandise to the customer 12. For example, thecustomer 12 may order from the distributor some level or amount ofmerchandise, goods, items, or products from M1, M2, and M3. As theinventory of these products is reduced, the distributor replenishes itsstock by placing orders with the respective manufacturers M1, M2, and/orM3. In this regard, the distributor acts as an intermediary in which thecustomer 12 need only interface with a distributor 14 for most or all ofits needs. Even if it deals only with one customer, the distributor canadd economic value by providing low cost storage to the customer.

FIG. 3 demonstrates yet another embodiment of a larger scale and can beillustrative of the current industry. Shown is the situation in whichmany customers C1 and C2 interface with many distributors D1 and D2.These distributors may interface with a plurality of manufacturers 10such as M1 through M4. Since not all distributors carry everymanufacturer's merchandise, the customer 12 may have to interface withmany distributors. In this regard, again a distributor may ship partialvehicle loads to the customer and the distributor may receive partialvehicle loads from the various manufacturers. Similarly, the distributormay ship partial loads to the customer. One well-understood benefit ofthis model is that, since each distributor services multiple customers,the total amount of stored goods required will be less than if the goodswere stored at each separate customer. Again, this model represents theindustry.

FIG. 4 is a Vendor Managed Inventory (VMI) model. As described herein,the VMI model permits open ordering in which the manufacturer monitorsthe distributor's inventory and replenishes it as needed. This is insharp contrast with the current paradigm in which the distributor placesorders with the manufacturer and maintains control over the orderingprocess. In FIG. 4, the VMI system 16 monitors the inventory level atthe distributor 14. When inventory levels drop, the VMI system 16,usually resident at the manufacturer's situs, sends purchase orders tothe manufacturer's shipment center to ship merchandise to thedistributor 16 for subsequent shipment to the customer 12. Because themanufacturer takes responsibility for ordering and transportation costs,it is able to send the order to the distributor without the distributoractually requesting each product, good, item, or merchandise.

FIG. 5 demonstrates a simple embodiment of the presently describedtechnology. Shown is the manufacturer 10 interfacing with a centralfacility or a cross-dock 18, which interfaces with the customers 12. Thecentral facility may be adapted to receive and process inventoryinformation of distributors or customers and then correlate thisinformation to shipments from the manufacturer to the customer. Onenon-exclusive purpose of the central facility or cross-dock is tomaximize transport vehicle capacity. The actual transport vehicle islargely inconsequential so long as the capacity of the vehicle can bedetermined. For example, it is well known that the standard truck has acapacity of about 44,000 pounds (around 2,000 cubic feet) and/or cancarry about 44 pallets of merchandise. Similarly, the standard train carhas a predetermined capacity. For example, a 50 foot boxcar has about6,235 cubic feet and a weight capacity of about 213,000 pounds. A 60foot boxcar has about 7,500 cubic feet and about 207,000 pounds ofweight capacity.

Thus, in its simplest form, maximization of vehicle capacity comparesthe maximum vehicle capacity measured against the capacity requirementsassociated with the merchandise initially ordered. The subtraction ofthese measurements yields the amount of unused capacity. Thus, newmerchandise may be added sufficient to fill up and/or substantiallyoptimize this unused capacity. This creates maximum or substantiallymaximum vehicle capacity. As used herein with respect to the presentlydescribed technology, the term “maximum” shall mean any amount orcapacity (e.g., in terms of volume, weight, or other applicableparameter) at a substantial level, including but not limited to thesubstantially greatest quantity or amount feasible or practical. In anyembodiment, though, the presently described technology can be modifiedto manage multi-pickup and multi-drop-off shipments, as well asshipments that travel between cross-docks. Per the presently describedtechnology, filling a vehicle can be done iteratively (while the vehicleis being loaded), or can be filled in advance by manipulating the ordersequence of order generation and/or vehicle optimization, before thegoods are finally ordered. As used herein with respect to the presentlydescribed technology, the terms “optimization”, “optimize”, and“optimizing” shall mean at a substantially optimal level in terms of alevel, an amount, a volume, a weight, or any other applicable parameter.

The filling/loading of the vehicle may concentrate on thefilling/loading of a single vehicle, or on providing a globallyoptimized solution that fills all vehicles going between variousdestinations. By shifting the load between multiple vehicles, a resultcan be attained that will be more optimal than first optimizing at theindividual vehicle level.

In another embodiment, once the vehicle capacity of a vehicle destinedto a particular destination is determined, for example, customer C1, anoptimization model can be engaged. In this regard, knowing (e.g., inadvance) that a partial truckload is destined from a shipper such as amanufacturer to a receiver such as a customer C1, the central facilitycan use this information to place additional orders with themanufacturer to increase the amount of merchandise on that shipment. Thevehicle is sent to the central facility or the cross-dock (if the twoare not at the same location) where the merchandise can be unloaded.Thus, a full truckload or substantially full truckload departs from themanufacturer M1. Similarly, merchandise may be sent from manufacturer M2and M3, etc., to the cross-dock too, thus having full or substantiallyfull trucks arrive at the cross-dock. At the cross-dock, the merchandiseare reorganized and/or commingled such that similarly destinedmerchandise are placed on the same vehicle and sent to the ultimatecustomer(s), such as customer C1. Thus, the presently describedtechnology permits trucks to travel full/loaded or substantiallyfull/loaded from the manufacturer(s) to the cross-dock, and fromcross-dock to customer(s).

By the way of example, the manufacturers may be large foodserviceindustry manufacturers, such as M1, M2, and M3, where M1 sells boxes ofketchup to a series of restaurants, M2 may sell boxes of plasticutensils, and M3 sells napkins. Customer C1 may be a restaurant chainthat requires ketchup, utensils, and napkins. In this regard, customerC1 could receive shipments from each manufacturer directly as in FIG. 1.However, the presently described technology substantially maximizestruckload capacity such that a substantially full truckload of ketchupboxes leaves M1, a substantially full truckload of utensil boxes leavesM2, and a substantially full truckload of napkins leaves M3. Bycollecting and reorganizing the merchandise at the cross-dock, ashipment comprising ketchup, utensils, and napkins is sent to customerC1. However, recognizing that the outbound vehicle also has a truckcapacity, if the capacity is not maximized, then the central facilitywill substantially optimize to add extra merchandise, such as moreketchup, utensils, or napkins onto the truck to substantially achievemaximum capacity. Since full or substantially full truckloads are sentfrom the manufacturer to the customer, significant savings are achievedand few LTL's are dispatched.

By way of further example, if the truckload capacity comprises 100boxes, and the Customer C1 destined initial shipment comprises 60%ketchup, 30% utensils, and 10% napkins, the extra merchandise added toobtain the 100 box capacity can be prorated among the percentages. Forexample, if after the initial load capacity is calculated it is foundthat another 10 boxes can be added to achieve maximum or substantiallymaximum truckload capacity, then this amount of boxes can be added toachieve the maximum or substantially maximum load. The extra 10 boxescan be prorated among ketchup, napkins, and utensils. Although shown asmanufacturers in FIG. 5, this model can also work with distributors. Theadditional merchandise need not be prorated though, as the additionalmerchandise can be the result of a bin-packing optimization model thataccounts for the three dimensional aspect of the vehicle (pallet layers,pallets, volume, cases, and weight) as well as the differences in themarginal value-added that come from shipping each additional incrementof a given product.

To maximize efficiency, the presently described technology may beconfigured to monitor the demand of the receivers or buyers, the levelsof “safety stock” needed to prevent stock-outs, the amount of stock onhand, any promotional stock needed, stock needed for seasonal demand,forecasts of stock demand, stock in transit, priorities of stock needed,etc. Prioritization may occur when the merchandise are needed atdifferent times, such as if the merchandise are perishables, if highrevenue merchandise are needed, high profit merchandise is needed, toprevent stock-outs, promotional seasonal, etc. Similarly, the system maybe configured to provide reports, such as printouts of the variousdemands, schedules, etc.

In another embodiment, the presently described technology may determinesubstantial optimization in a predetermined manner prior to shipping. Itis capable of coordinating the shipments from shipper(s) to receiver(s)even before the first shipment actually leaves. In this regard, thepresently described technology generates orders for its customers versusgenerating orders in response to the customer's request. The presentlydescribed technology may arrange for and substantially optimizes thetransportation and order flow simultaneously, thus pre-scheduling most,if not all, of the shipping components. Since title to the goods remainseither with the shipper or receiver, the company operating the presentlydescribed technology need not take title to the goods.

FIG. 6 demonstrates another embodiment of the presently describedtechnology in which receivers, such as distributors are involved. Inthis model, a plurality of distributors 14 transport merchandise to aplurality of customers 12. The central facility, which may include thecross-dock 18 may coordinate inventory and orders at the distributor.Again, it should be noted that the cross-dock need not be collocatedwith the central facility. In this model, a VMI-like system may be usedin conjunction with the central facility. Accordingly, as the centralfacility monitors the distributor's inventory, the central facilityprepares to order the merchandise on behalf of the distributor. Thecentral facility, such as cross-dock 18, monitors the merchandise to beshipped to the distributor. The central facility also has enoughinformation to determine on its own if an outgoing truck is full or not.If the truck to be dispatched is not full, the central facility willsend an order for more merchandise to be added to the level that willfill or substantially fill the truck. Similarly, the central facilitywill monitor shipments originating at the other manufacturers such as M2and M3. In essence, the optimization model creates an order plan forfull or substantially full shipments from the manufacturers before it isshipped or before the order is finalized. The coordination with othershipments in the supply chain with the central facility monitoringsystem is also available.

In any embodiment, the external packaging, external labels, SKU codes,pallet tags, UPC codes, etc., may classify the merchandise. Merchandiselacking any indicia may be tagged in any manner to identify themerchandise. “SKU” stands for a Stock Keeping Unit, which is anidentification number assigned to a unique item or a unique type of itemby the retailer. The SKU may be an internal number to that retailer ormay be tied to an item's UPC (Universal Product Code), EAN (the EAN-UCCidentification number), EPC (Electronic Product Code in relation to RFID(Radio Frequency Identification) systems and the like), and GS1 GDSN(GS1 Global Data Synchronization Network, an alternative to the EANsystem). Accordingly, the commingling of merchandise is maximized whenthe merchandise are adequately identified. Naturally in somecircumstances, not all merchandise arriving at the cross-dock aredestined for the same place. Accordingly, it may be necessary todetermine the destinations of each item and further label or track itsdestination. Thus, marking products with unique destination indicia canfacilitate the process of determining destinations of merchandise.

In one embodiment of the presently described technology, a shipmentfrom, for example, M1 can go directly to the distributor D1. Similarly,shipments from M2 can go directly to D1 also. Similarly destinedmerchandise, such as merchandise going to the same customer C1, can becoordinated such that merchandise from a variety of manufacturers are onthe same truck. If the truck is not full/loaded, then the centralfacility will monitor the capacity and order more merchandise to beloaded onto the truck until it is full/loaded or substantiallyfull/loaded. Thus, a full/loaded or substantially full/loaded truck willarrive at the customer C1. As described more fully herein, theoptimization model may consider the option of putting or not putting thetruck through the cross-dock.

In another embodiment, the merchandise from the manufacturer may arriveat a cross-dock 18 and its merchandise may commingle with merchandisefrom other manufacturers. The cross-dock permits loading of similarlydestined merchandise for shipment to the same distributor or to the samecustomer. It should be noted that the system does not just monitortruckload capacity. Rather, it arranges for truckload capacitysufficient to transport the required product.

Thus, in one exemplary model, the cross-dock or central facility mayperform some or all of the following steps of receiving forecasts ofcustomer demand for a product: monitoring truckload capacityrequirements, arranging orders in such a way that more merchandise isfilled or loaded into the truck, commingling the merchandise with otherparty's merchandise, loading similarly destined merchandise onto thesame truck, adding more merchandise if the truck is not full/loaded, andthen sending this truck along to a destination, such as anotherdistributor or a customer. The optimization model can take into accountthe relative schedules of shipments in advance to coordinate arrivals atthe cross-dock and outgoing shipments from the cross-dock.

In yet another embodiment of the presently described technology, it isnot necessary to commingle merchandise arriving at a cross-dock ofvarious manufacturer's merchandise at the same time. For example, usingthe models of FIG. 5 and FIG. 6, a full or substantially full truckloadof merchandise may arrive at the cross-dock 18 or distributor 14. Thesenewly arrived merchandise may be commingled with merchandise that havebeen earlier inventoried at the cross-dock or distributor. Merchandiseof a similar destination are then placed on the outgoing truck. Anyempty capacity can then be filled up with older or lower prioritymerchandise from the cross-dock or distributor.

In yet a further embodiment, the presently described technology furtherenvisages the coordination of pick-ups and drop-offs of shipments amongcustomers (e.g., C1, C2, etc.), manufacturers (e.g., M1, M2, etc.),and/or distributors (D1, D2, etc.), for example, through a centralfacility and/or cross-dock. Such coordinated picking up and dropping offof shipments allows each customer, manufacturer, and/or distributor(i.e., collectively “members” utilizing the presently describedtechnology) to schedule such shipment activities in a manner that ismutually beneficial. For example, a member can schedule a truck that hastaken product to one receiver to then pick up product from somewherenear that receiver's location and deliver that product to a secondreceiver location somewhere near the original shipping location (e.g.,the original departure point of the truck). Thus, where the truck wouldoriginally depart with shipment for one “member” and return to itsoriginal departure location empty, the truck now also picks-up anddrops-off shipments to other “members” (i.e., C's, M's, or D's)utilizing the presently described technology as well. Such a coordinatedoption is not available in systems that do not allow for or offercoordination between its same or different “members”.

One simple implementation of optimization technology to the currentinvention can be viewed as a variant on the well-understood maximum flowmethod developed by Ford and Fulkerson. This approach makes somesimplifying assumptions. Only one set of cost constraints applies (e.g.,product density per unit shipped is sufficiently high to ensure thatweight will always be the constraint). Additionally, the goods shippedis assumed to be either continuous or sufficiently discrete to permithigh granularity of shipments. In addition, each type of product isavailable from only one geographic source. Finally, all shipments underthis simple model are assumed to pass through a single cross-dock.

To apply this technique to the problem, each combination of source,destination, and product type (e.g., SKU) is assigned a value associatedwith a performance metric, a single cost constraint (e.g., weight), theratio of performance metric to cost constraint, a minimum amount toship, and a maximum amount to ship. In addition, the algorithm uses amatrix or list of nodes, including sources of goods, destinations ofgoods, and cross-docks, as illustrated in FIG. 5 and FIG. 6.

Under this approach, the computer running the program traverses the listof source-destination-SKU combinations to determine the minimum shipmentrequirements for each source-destination-SKU combination. The programalso creates and generates a list of sources and destinations thattracks the amount of shipping required to move goods between each sourceand each destination via the cross-dock. The result of this step is amatrix that lists each combination of source and destination, and thetotal amount of shipping capacity required to transport the requiredminimum shipment of goods from its respective source to its respectivedestination.

Furthermore, the computer with memory running the program also traversesthe source-destination-SKU list to determine the amount of shippingrequired to ship the amount of goods that must be shipped. Since thisimplementation assumes only a single cross-dock, vehicle capacity mustbe assigned to the trip from the source to the cross-dock and from thecross-dock to the destination. Whenever insufficient vehicle capacityexists to carry all mandatory orders on a given route into or out of thecross-dock, another vehicle is assigned to that route. Assigning goodsto a vehicle and assigning a vehicle to a route changes the amount ofexcess capacity available to carry discretionary goods on that route.

Eventually the computer with memory running the program processes themandatory orders for all source-destination-SKU combinations. Thisoperation results in a set of unused vehicle capacities from each sourcethat has shipped mandatory orders into a cross-dock, and from thecross-dock to each destination that will receive mandatory orders ofgoods that have passed through cross-dock.

Once the total shipping capacity required to move the required number ofgoods between any source and destination is determined, the computerwith memory operating the program then sorts the list ofsource-destination-SKU combinations by the ratio of the performancemetric to the cost constraint. This process yields a list that providesthe order in which the program should evaluate adding discretionarygoods to the order plan and to the shipping capacity that travelsbetween a given source and destination.

The computer with memory then traverses the sorted list ofsource-destination-SKU combinations. For each source-destination-SKUcombination, it determines if additional discretionary orders arepossible, if spare capacity exists going from the source to thecross-dock, and from the cross-dock to the destination. It alsocalculates the minimum of the amount of discretionary orders available,shipping capacity into the cross-dock, and capacity out of thecross-dock. This number is the maximum or substantially maximum amountof discretionary orders that can be placed, given the number of vehiclesassigned to each route (e.g., maximum or substantially maximum andfeasible order size).

At this point, the computer with memory running the program adds anorder in the amount of the maximum feasible order size to the orderplan, and reduces the available capacity going from the source to thecross-dock and from the cross-dock to the destination by the combinedcost constraint represented by the amount of the maximum feasible ordersize.

This procedure is repeated for each successive member of the sortedsource-destination-SKU list until the list is traversed or there is nomore available capacity/substantial capacity. The computer thengenerates a source-destination-SKU list that denotes the amount of eachgood ordered from each source by each destination. It also generates alist or shipping plan denoting how many items are being shipped fromeach source through the cross-dock to each destination, and on whatvehicle they will be transported.

This relatively simple method can be supplemented by allowing for thepossibility that shipments can travel directly from the source to thedestination without passing through the cross-dock, or that a given pathbetween a source and destination can include either multiple sources ofproduct (multiple pickup) or multiple destinations (multiple drop-off).

A more complete approach of the presently described technology usesinteger linear programming to solve a multistage transshipment problem.In this case, the system is again modeled as a network of sources,destinations, and cross-docks. In this case, the algorithm maximizes thedifference between positive (e.g., revenue) and negative (e.g., cost)performance metrics, subject to the usual constraints found in atrans-shipment problem, including vehicle capacity (e.g., height,weight, width, length, volume), non-negativity of shipment quantities,zero product left at a cross-dock, etc.

An additional extension of the presently described technology wouldinclude the ability to commingle products traveling between differentlegal entities with those of the same entity. Thus, for example, thepresently described technology may note that product is required at afacility in Houston, and that there is a large supply of product at afacility in Dallas owned by the same distributor. In this case, thepresently described technology may be able to determine that thesubstantially optimal solution to the problem would involve addingproduct from the Dallas facility to a vehicle traveling from Chicago toHouston via Dallas.

The Ford-Fulkerson models are described in the following articles, thedisclosures of which are expressly incorporated by reference herein: L.R. Ford, Jr. and D. R. Fulkerson, Maximal Flow Through a Network,Canadian Journal of Mathematics, 8:399-404 (1956); L. R. Ford, Jr. andD. R. Fulkerson, A Simple Algorithm for Finding Maximal Network Flowsand an Application to the Hitchcock Problem, Canadian Journal ofMathematics, 9:210-218 (1957); and L. R. Ford, Jr. and D. R. Fulkerson,Flows in Networks, Princeton University Press, Princeton, N.J. (1962).Other models include branch and bound algorithms.

Technology also may be derived from other simulation oriented softwaresuch as “war games” or chess software that play out variouspermutations, combinations, or solutions, predicts the best “move” andexecutes it.

Another implementation of the presently described technology optimizesshipments of standardized pallets for each given SKU on standardizedvehicles. This approach further assumes that a profit-maximizing firmreceives revenue from manufacturers to deliver product from a source Sto a destination D over a fully connected network of nodes N, which maybe sources, destinations, or transshipment points. In this approach, thefirm selects routes R for pallets and r for vehicles, both of whichconsist of an ordered finite list of nodes. Routes R or r may alsoinclude no elements, which denotes that the pallet is not shipped, orthat the vehicle is not employed.

For this approach, the optimization problem can be represented as avariant of transshipment problem in which the two sets of controlvariables are the number of pallets of product type SKU traveling invehicle V on route R from source S to destination D, X_(SKU,V,R,S,D)′and the route of each vehicle V, r_(V).

${\begin{matrix}{Max} \\{x_{{SKU},V,R,S,D,}r_{V}}\end{matrix}{\sum\limits_{SKU}{\sum\limits_{S}{\sum\limits_{D}{{{Income}\left( {{SKU},S,D} \right)}x_{{SKU},V,R,S,D}}}}}} - {\sum\limits_{V}{{VehicleCost}\left( {r_{V},V} \right)}} - {\sum\limits_{n}{\sum\limits_{SKU}{{{PerNodeCost}\left( {x_{{SKU},V_{1},R,S,D,i,n},x_{{SKU},V_{2},R,S,D,n,j}} \right)}x_{{SKU},V_{1},R,S,D,i,n}}}}$

The above objective function for the firm consists of three differentelements. The first is the revenue function for shipping a pallet oftype SKU to a destination D, times the number of pallets of product typeSKU shipped from source S to destination D. This formulation of therevenue function permits the possibility of the firm receiving differentlevels of revenue from the manufacturer depending where the firm picksup the product from the manufacturer.

The first cost component is the cost of running all vehicles V along allroutes r_(V). The second cost component represents the total cost of allpallets of type SKU traversing a node n. In this expression, theexpression x_(SKU,V,R,S,D,i,n) represents the number of pallets ofproduct type SKU moving on vehicle V following route R from source S todestination D that travel between nodes i and n. Note that theformulation of this function permits the pallets to arrive at node n onone vehicle and leave it on another. Thus, the per node cost can be usedto account for cross-docking fees as the pallet, moving on route R onvehicle V₁, arrives at node n from node i, and is transferred to vehicleV₂ moving to node j. In this formulation, the PerNodeCost is expressedon a per pallet basis, and can vary as a function of the product type.Note that, although V₁ and V₂ are separate variables, they can bothrefer to the same vehicle. Note also that this system can be used toaccount for pickup or delivery costs by setting i to S or j to D,respectively.

This system is also subject to a set of constraints. Among them areconstraints on the number of pallets that can be shipped on a givenvehicle:

${\sum\limits_{SKU}x_{{SKU},V,R,S,D,i,n}} \leq {{Max}\; {{PalletsPerVehicle}(V)}}$

where MaxPalletsPerVehicle is 44 for a typical trailer, but can vary,depending on the type of vehicle used as described herein. Thisconstraint applies whenever the pallets move on a vehicle.

Similarly, the weight constraint must be met:

${\sum\limits_{SKU}{{{WeightPerPallet}({SKU})}x_{{SKU},V_{1},R,S,D,i,n}}} \leq {{Max}\; {{WeightPerVehicle}(V)}}$

where MaxWeightPerVehicle would be about 44,000 lbs. for a typicaltrailer. Again, this parameter is a function of vehicle type asdescribed herein.

In this simplified case, since a pallet size is standardized, it isassumed that the volume constraint is accounted for by the pallet countconstraint.

A non-negativity constraint must also be met for shipments:

x_(SKU,V,R,S,D,i,n)≧0

This constraint applies for all SKU, V, R, S, D, i, and n.

Finally, there is the flow constraint on each node, where the net flowof product through a node must exceed some minimum value, and must notexceed some maximum:

${\sum\limits_{V}\left( {x_{{SKU},V,R,S,D,i,n} - x_{{SKU},V,R,S,D,n,j}} \right)} \leq {{Max}\; {{NetNodeFlow}\left( {{SKU},n} \right)}}$${\sum\limits_{V}\left( {x_{{SKU},V,R,S,D,i,n} - x_{{SKU},V,R,S,D,n,j}} \right)} \geq {{Min}\; {{NetNodeFlow}\left( {{SKU},n} \right)}}$

where MaxNetNodeFlow and MinNetNodeFlow are the maximum and minimumvalue for the number of pallets that enter the node, less the numberthat leave. For a source, these numbers are typically negative. For adestination, these numbers are expected to be positive. For atransshipment point, these numbers typically zero. The above constraintapplies to all nodes, whether they are sources, destinations, orcross-docks. The only difference between these three different types ofnodes is the value of the parameters MaxNetNodeFlow and MinNetNodeFlow,which are functions of the node and the SKU.

If the objective function and the constraints can be formulated aslinear functions, a linear program can be formulated based on thisproblem and solved.

FIG. 7 demonstrates one prior art system for VMI management. This systemis based on the IBM Continuous Replenishment Process (CRP) VMI system.Essentially, one part of the IBM VMI system records the inventory of thedistributor at the day's close. This part then transmits the informationto the main VMI server. The server prioritizes optimal or substantiallyoptimal shipment levels. This information is then transmitted to thedistributor's purchasing department and the manufacturer's VMI systemoperator for approval. The manufacturer's VMI then receives a purchaseorder from the VMI server and acknowledges receipt of the purchaseorder. The VMI server also sends an acknowledgement to the distributorthat the manufacturer has accepted the VMI purchase order. Meanwhile,the manufacturer's VMI system operator then cuts a sales order at themanufacturer site and processes a shipment. An order acknowledgement andan advance shipping notice is sent from the VMI server to thedistributor notifying it about the order, contents, estimated time ofarrival, price, etc. The merchandise is then shipped from themanufacturer to the distributor. As can be seen, this is a typical VMIsystem in which because of the “open books” format of the distributor,the manufacturer can regulate the inventory levels at the distributor.

FIG. 8 demonstrates an embodiment of the presently described technologyintegrating the IBM VMI system. The presently described technology mayalso include the allocation resource protocol set forth in U.S. Pat. No.5,216,593 (issued 01 Jun. 1993); or the optimized logistics plannerdisclosed in U.S. Pat. No. 5,450,317 (issued 12 Sep. 1995); or theintegrated monitoring system disclosed in U.S. Pat. No. 5,983,198(issued 9 Nov. 1999); the disclosures of which are expresslyincorporated by reference herein. As before, the VMI system records theinventory levels at the distributor. This information is sent to the VMIserver, which correlates optimal shipment levels outbound from thecross-dock for each manufacturer and prioritizes merchandise. Anindependently managed inventory system provider (IMI) system of thepresently described technology reads the VMI information, such as theoptimized shipping schedules at the distributor site. Based on thevehicle capacity, the IMI system of the presently described technologygenerates another set of purchase orders. This new set of orders may,but need not be, taken to the distributor's purchasing manager forapproval. This new set of orders may reflect the cost savings forsubstantially optimizing the truckload. The approved order is sent tothe manufacturer and the cross-dock. The central facility substantiallyoptimizes the shipment from the manufacturer into the cross-dock byarranging for pick up, etc. In the meanwhile, the approved order arrivesat the manufacturer for approval, processing, and subsequent shipmentfrom the manufacturer to the cross-dock. Merchandise arrive at thecross-dock and are substantially optimized with other merchandise goingto the same distributor. Ultimately, the merchandise of a variety ofmanufacturers arrive at the distributor. Thus, as shown, the IMI can bean independent third party company, that is, a company not related tothe distributor or manufacturer.

FIG. 9 demonstrates an embodiment in which the distributor iseliminated. In this embodiment, by refining the calculations, near fulltruckload capacity can be achieved without using a distributor. In thisexample, the IMI system may be part of the customer's facility in whichthe cross-dock IMI system monitors the inventory level at the customer.The cross-dock IMI assembles and correlates the inventory levels acrossall the customers. Thus, the cross-dock IMI derives a truckload capacityand the requirements of each customer. This information is substantiallyoptimized and sent to the various manufacturers. Once it is determinedwhat vehicle the manufacturer will use for transport, the IMI systemwill substantially optimize the capacity utilization of the vehicle byadding more merchandise to the truck. Meanwhile, this process continuesacross all the vehicles receiving goods from all the manufacturers. Inthis regard, this creates substantially maximum shipping capacity fromthe manufacturers to the cross-dock. The merchandise are then unloadedand reassembled into similar destinations. Since the IMI has alreadysubstantially optimized what merchandise are needed by the customers,the cross-dock system will collect similarly destined merchandise andsubstantially maximize truckload capacity to the customer. Vehicle sizesuch as truck size can be adjusted by using smaller trucks or largerones as needed.

Since technology permits logistics to be computerized, the presentlydescribed technology may partially reside in a computerized form. Forexample, the presently described technology may include a computerprogram embodied on a tangible medium, such as a disk drive, CD ROM,network, floppy disk, zip drive, or server, to optimize shipment ofmerchandise on a vehicle by filling/loading or substantiallyfilling/loading the vehicle. The computer program may include a firstset of instructions to determine a vehicle load capacity; a second setof instructions to determine a shipment requirement or discretionaryorder; a third set of instructions to generate a comparison by comparingthe vehicle load capacity with the shipment requirement; and a fourthset of instructions to load more merchandise on the vehicle if thecomparison indicates that the vehicle is not yet full/loaded orsubstantially full/loaded. These instructions may also code formonitoring the inventory levels at the distributor, manufacturer,customer, or cross-dock.

The presently described technology may also reside in a signal. Thesignal may further include other signals that: (a) signal the inventorylevel at the customer, manufacturer, distributor, or cross-dock; (b)identify maximum vehicle load capacity; (c) facilitate replenishment ofthe vehicle if the vehicle is not yet full; (d) facilitate correlationsat the cross-dock; (e) provide feedback to the manufacturer,distributor, customer, or cross-dock; (f) provide a purchase ordergeneration and confirmation system; or (g) otherwise permit vehiclecapacity to be maximized.

It is appreciated by those skilled in the art that the process shownherein may selectively be implemented in hardware, software, or acombination of hardware and software. An embodiment of the process stepsemploys at least one machine-readable signal-bearing medium. Examples ofmachine-readable signal-bearing mediums include computer-readablemediums such as a magnetic storage medium (i.e., hard drives, floppydisks), or optical storage such as compact disk (CD) or digital videodisk (DVD), a biological storage medium, or an atomic storage medium, adiscrete logic circuit(s) having logic gates for implementing logicfunctions upon data signals, an application specific integrated circuithaving appropriate logic gates, a programmable gate array(s) (PGA), afield programmable gate array (FPGA), a random access memory device(RAM), read only memory device (ROM), electronic programmable randomaccess memory (EPROM), or equivalent. Note that the computer-readablemedium could even be paper (e.g., tape or punch cards) or anothersuitable medium, upon which the computer instruction is printed, as theprogram can be electronically captured, via for instance opticalscanning of the paper or other medium, then compiled, interpreted orotherwise processed in a suitable manner if necessary, and then storedin a computer memory.

Additionally, machine-readable signal bearing medium includescomputer-readable signal-bearing mediums. Computer-readablesignal-bearing media have a modulated carrier signal transmitted overone or more wire-based, wireless or fiber optic networks or within asystem. For example, one or more wire-based, wireless or fiber opticnetwork, such as the telephone network, a local area network, theInternet, or a wireless network having a component of acomputer-readable signal residing or passing through the network. Thecomputer-readable signal is a representation of one or more machineinstructions written in or implemented with any number of programminglanguages.

Furthermore, the multiple process steps implemented with a programminglanguage, which comprises an ordered listing of executable instructionsfor implementing logical functions, can be embodied in anymachine-readable signal bearing medium for use by or in connection withan instruction execution system, apparatus, or device, such as acomputer-based system, controller-containing system having a processor,microprocessor, digital signal processor, discrete logic circuitfunctioning as a controller, or other system that can fetch theinstructions from the instruction execution system, apparatus, or deviceand execute the instructions.

In FIG. 10, a message flow diagram 1000 for the optimizing transportvehicle load capacity process is shown. A server 1002, such as a VMIserver, sends and receives messages from a distributor 1004 and amanufacturer 1006. A distributor 1004 sends a periodic inventory message1008 to the server 1002. The periodic inventory message 1008 ispreferably sent every business day, but in alternate embodiments may besent hourly, daily, bi-weekly, weekly, monthly, or some upon some othertriggering event (e.g., changes in inventory level). The periodicinventory message is formatted so information contained in the messagecorresponds to the removed or sold distributor inventory. The server1002 receives the periodic inventory message 1008 and processes it usinginformation about the inventory needs stored in a database. The databasecontains information about the type and amount of inventory normallymaintained by the distributor 1004.

The server 1002 also has access to vehicle load sizes that are alsostored in the database. The server 1002 determines the optimal shipmentto meet the inventory needs of the distributor 1004 and sends an optimalshipment order message 1010 to the manufacturer 1006. The server 1002then receives an order acknowledgement 1012 from the manufacturer 1006signifying that the order has been received. The server 1002 sends anorder acknowledgement message 1014 to the distributor 1004 in responseto reception of the order acknowledgement message 1012 from themanufacturer 1006. A sales order 1016 is also sent from the manufacturer1006 to the distributor 1004.

In FIG. 11, a server 1100 that performs the optimizing transport vehicleload capacity process is shown. The server 1100 is made up of a numberof components including a controller 1102 connected to a data bus 1104.The data bus is connected to a communication port 1106, a RFcommunication port 1108, an internal storage medium 1110, aninput/output port 1112, a memory 1114, and a printer port 1116. The RFcommunication port 1108 is connected to the data bus 1104 and an antenna1118 for reception of RF signals 1120. The communication port 1106 isconnected to the data bus 1104 and a public switched telephone network(PSTN) 1122. The printer port 1116 is connected to the data bus 1104 andthe output device 1124 (printer, video display, LCD display, or anyother device capable of generating an output viewable by a human). Theinput/output port 1112 is connected to the data bus 1104 and an externalstorage device 1126. The memory 1114 contains database tables 1128,report formats 1130 and machine readable code 1132.

As shown in FIG. 10, a distributor 1004 sends a periodic inventorymessage 1008 via the PSTN 1122 (see FIG. 11) to the server 1100. Theserver 1100 receives the periodic inventory message 1008 (see FIG. 10),at the communication port 1106 (see FIG. 11). The controller 1102accesses the periodic inventory message 1008 (see FIG. 10), over thedata bus 1104. The controller 1102 accesses the database tables 1128 todetermine what inventory the distributor 1004 (see FIG. 10) requires.The controller 1102 executing the machine-readable code 1132, such as“C++” code, identifies one or more vehicle(s) and vehicle load sizecontained in the database tables 1128. The controller then generates anoptimal shipment order 1010 (see FIG. 10). The optimal shipment ordercan then be printed out to an output device 1124 (see FIG. 11) by theprinter port 1116 and sent to the manufacturer 1006 (see FIG. 10) by thecommunication port 1106 (see FIG. 11) via the PSTN 1122. The format ofthe printed out substantially optimal shipment order 1010 (see FIG. 10)is determined by the report format 1130 contained in the memory 1114 ofthe server 1100. In alternate embodiments, a different type ofcommunication network other than a PSTN 1122 may be accessed, such as apacket-switch network, wireless network, hybrid-fiber network, LAN, WAN,or a combination of networks.

The controller 1102 generates the optimal shipment order message tosubstantially maximize the capacity utilization of one or morevehicle(s) from the manufacturer 1006 to the distributor 1004. After theoptimal shipment message 1010 is sent to the manufacturer 1006, theserver 1002 receives an order acknowledgement message 1012 from themanufacturer 1006 at the communication port 1106 via the PSTN 1122. Thecontroller 1102 formats an order acknowledgement message 1014 (see FIG.10) for the distributor 1004 upon receipt of the order acknowledgementmessage 1012 from the manufacturer 1006. Additionally, the manufacturer1006 may send a sales order 1016 directly to the distributor 1004. Theserver 1002 may also cut a purchase order to the carrier via thecommunication port 1106.

As with any embodiment, the system may also include vehicles equippedwith satellite tracking systems, such as a Global Positioning System.For example, the system may include a QTRACS system manufactured byQualcomm, Inc. to monitor vehicle position. In this regard, coordinationat the cross-dock may be facilitated knowing that inbound trucks arecoming, or otherwise provide dynamic shipping information. In addition,the tracking permits rapid communication with the customer to informthem that a truck is expected soon or that the truck is remaining onschedule. As with any embodiment herein, all communications betweenunits or components, may be via cellular, telephone lines, satellite,wireless, etc. In other embodiments, the GPS technology may be utilizedwith the pallets, boxes, cartons, or the like themselves. In particular,GPS may be used with high value items so that tracking these items isfacilitated. In other embodiments, using transponders, such as RFtransponders, the pallets or goods themselves could be tracked to seewhat goods are on what truck. If GPS is used with the truck, then itbecomes rudimentary to know what goods (e.g. what pallets) are where atall times.

The server 1100 is able to receive global positioning service (GPS) dataabout vehicle positions from an RF communication port 1108. Thecontroller 1102 correlates the data about the vehicle positions in orderto identify a vehicle to carry the shipment. The vehicle selection andinventory requirements are both used by the controller 1102 to identifythe optimal shipment order. The controller 1102 also receives vehicleposition data from the RF port 1108, and uses it to determine estimateson arrival times to a cross-dock, correlates these arrival times, andmodifies shipping schedules to substantially optimize logistics costs.In an alternate embodiment, the GPS data is received at the server 1100via the communication port 1106.

As with any embodiment described herein, the merchandise may beprioritized based on any immediate, medium term, or long term needs.Accordingly for example, immediately needed merchandise at thecross-dock can be substantially optimized with medium term neededmerchandise. Similarly, the optimization function may be performedconcurrently with order placement or before. The optimization may bebased on a single vehicle, or by obtaining a globally and substantiallyoptimized value across a plurality of vehicles. Similarly, as with anyembodiment, there may be single or a plurality of manufacturers ,distributors, customers, or cross-docks. The system can accommodatemultiple pick-ups and drop-offs on vehicle trips between the shipper andreceiver.

Similarly, the various entities involved may be geographically closelylocated, or quite some distance apart. In one embodiment though, havingthe cross-dock in relatively the same geocenter will facilitateimplementation of the system. In addition, as with any embodimentherein, the system may be divided up so that various components are notin the same location. For example, order processing can begeographically remote from any other entity, such as the cross-dock orthe manufacturers. On the other hand, system implementation may occur ingenerally the same location or at the same facility, such as if most ofthe IMI system is at the distributor facility. In addition , it shouldbe recognized that the legal entity receiving the goods could be adifferent entity than the one that actually receives the goods. Forexample, Company X headquartered in California may be the legal entity“receiving” the goods, but the actual shipping location to receive thegoods could be in Illinois. It should also be appreciated that thepresently described technology may include many cross-docks, either allor some located in the same geocenter; and/or all cross-docks indifferent geocenters. It should also be appreciated that the presentlydescribed technology may schedule shipments that may require products topass through multiple cross-docks.

In yet another embodiment, the presently described technology may beadapted to provide shipping to remote locations not currently accessibleby road. For example, most shipping to the Hawaiian Islands is via boat.However, the presently described technology may be adapted to coordinateand substantially optimize shipments of goods from across the country(or the world) into the shipping port, for subsequent shipment toHawaii.

FIG. 12 also demonstrates another feature of the presently describedtechnology. The optimization step may further include the step ofexercising discretionary control over the products to be shipped. Inthis regard, higher priority goods may be shipped and lower prioritygoods not shipped for later shipment. Thus, the presently describedtechnology contemplates the step of prioritizing the products to beshipped. The presently described technology also includes the ability tooptimize shipments for horizontal integration across different legalentities. The presently described technology also includes the abilityto vertically integrate where multiple shipments across time are nowconsolidated into one shipment. Thus, the presently described technologyincludes the step of substantially optimizing the product shipmenttemporally among at least one other shipment.

Thus, many features of the presently described technology are realizedsingularly or in combination, such as, but not limited to, theprioritization step further including the step of determining at leastone of the following steps:

-   -   (a) calculating a mix of additional products to be added to at        least part of the shipment when a total amount of product        shipped is greater than a minimum amount of product initially        ordered;    -   (b) calculating a mix of additional product to be added to at        least part of the shipment when the maximum vehicle load is not        exceeded;    -   (c) scheduling the shipment from the plurality of shippers to        arrive at a cross-dock before shipping the product to the at        least one receiver; and    -   (d) substantially optimizing the optimization metric.

Accordingly, the presently described technology also includes the stepof manipulating the shipment at a cross-dock in the manners describedherein. This may include the use of destination indicia and may furtherinclude ensuring that products entering the cross-dock have a predefineddestination beyond the cross-dock. As mentioned herein though, thecross-dock is not critical to the operation of the presently describedtechnology. For example, optimization may occur without the physicalcross-dock. Two trucks operating within the presently describedtechnology system may meet somewhere, such as a truck stop or rest stop.In one example, the first truck unhitches its trailer and re-hitches itto the second truck. In this manner, the presently described technologycontemplates that optimization of these trailers may be in order tosubstantially maximize that one truck carrying two trailers arrives at areceiver. In another example, the contents of the first truck may bepacked into the second truck so that the second truck capacity issubstantially maximized, without the use of formal cross-dock.

Therefore, one embodiment of the presently described technologycomprises a method of substantially optimizing a shipment of at leastone product from a plurality of shippers to at least one receiver, theplurality of shippers comprising different legal entities; or a methodof substantially optimizing shipments from a plurality of shippers to aplurality of receivers; or a method of substantially optimizingshipments from at least one shipper to at least one receiver, thepresently described technology comprising the steps of determining amaximum or substantially maximum load of at least one transport vehiclefrom the shippers; and substantially optimizing the maximum orsubstantially maximum load of the least one transport vehicle.

As with any embodiment, optimization may include one or more factors,such as the step of determining at least one of a substantially maximummass, maximum length, maximum height, maximum width, maximum volume, andpallet footprint of the at least one transport vehicle. Optimization mayfurther include the step of establishing at least one optimizationmetric, which may include but is not limited to, a metric establishingstep which further includes the step of establishing at least one of thefollowing metrics: a capacity utilization per vehicle mile, totaltransportation cost metric; transportation cost as percentage of productvalue shipped metric; total logistics costs; shipping revenue metric;and shipping revenue less freight cost metric.

As an inducement to participate, the presently described technology alsocontemplates the providing of a trade allowance to the receiver, forexample, from the IMI to the receiver. The trade allowance may include,but is not limited to, a rebate. Other inducements such as percent off,coupons, rebates, premium give-away, or other such commonly knownfeatures are expressly contemplated.

The presently described technology also allows for a profit sharingprogram, which is a further benefit to manufacturers, distributors, andin particular, customers. For example, for any given route run (theseries of pick-ups and drop-offs a truck goes through before returningto its original starting location) where there are at least twocustomers, distributors, or manufacturers involved (in any combination),a gross margin percentage may be calculated from taking the totalrevenue generated by the route and subtracting the total costs of theroute. In doing so, one is able to calculate the percentage remaining ofthe total revenue generated from the operation of a particular route.For each member (i.e., C, M, or D), that percentage remaining is thenmultiplied by that member's gross revenue from the particular route runto determine the amount of profit sharing.

More specifically, by way of one illustrative example, assume a customerC1 had $1,000 dollar revenue generated from the run/route, customer C2had $2,000 dollar revenue generated from the route run, and the grossmargin percentage was 30%. Utilizing the profit sharing program of thepresently described technology, customer C1 would receive 30% of $1000and customer C2 would receive 30% of $2000 as their respective profitsharing for the particular run/route.

It should be understood that the foregoing relates only to a limitednumber of embodiments that have been provided for illustration purposesonly. It is intended that the scope of invention is defined by theappended claims and that modifications to the embodiment above may bemade that do not depart from the scope of the claims.

We claim:
 1. A system for optimizing the shipment of merchandise, saidsystem including: one or more transport vehicles, said one or moretransport vehicles transporting a shipment from a first shipper and asecond shipper to a first receiver and a second receiver, wherein saidfirst receiver maintains a first inventory of merchandise at a firstreceiver location, wherein a first inventory determination determinesthe limits of merchandise required to be maintained for said firstinventory according to one or more first metrics in response to firstdata received from said first receiver, wherein said second receivermaintains a second inventory of merchandise at a second receiverlocation, wherein a second inventory determination determines the limitsof merchandise required to be maintained for said second inventoryaccording to one or more second metrics in response to second datareceived from said second receiver; and computer hardware, computersoftware stored on a computer and operating on a computer or acombination of computer hardware and computer software stored on acomputer and operating on a computer determining a shipment ofmerchandise, wherein said shipment of said merchandise is optimized bydetermining one or more maximum loads of said one or more transportvehicles at least in part by calculating an amount of merchandise forshipment by said one or more transport vehicles from said first andsecond source locations to said first and second receivers that reduceslogistics costs and that results in shipment of merchandise within thelimits of merchandise required to be maintained for the first inventoryand within the limits of merchandise required to be maintained for thesecond inventory according to a calculation employing at least said oneor more first metrics, said one or more second metrics, said first data,and said second data.